In machine learning (ML), bias has become a priority concern for fair and inclusive development of ML technologies. A ML system is considered biased if its predictive outputs favour or are prejudiced against individuals or groups of people based on their demographic attributes [1]}. An often-times unintended consequence of bias is discrimination, which can lead to public mistrust [2]} and legal consequences [3]}. It is thus important that potenatial bias in SV systems is evaluated and mitigated during development. Studies of bias in SV, and research to mitigate its effects, are currently limited. Recent research has highlighted that existing SV evaluation practices contribute to exacerbating bias in the development of SV systems [4]}. In particular, unrepresentative benchmarks make it impossible to evaluate bias, let alone address it.
